An Adaptive Grid Algorithm for Air Quality Modeling

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The physical and chemical processes responsible for air pollution span a wide range of spatial scales. For example, there may be point sources, such as power plants that are characterized by relatively small spatial scales compared to the size of the region that may be impacted by such sources. To obtain accurate distributions of pollutants in an air quality simulation, the pertinent spatial scales can be resolved by varying the physical grid node spacing.A new dynamic adaptive grid algorithm, the Dynamic Solution Adaptive Grid Algorithm - PPM (DSAGA-PPM), is developed for use in air quality modeling. Given a fixed number of grid nodes, DSAGA-PPM distributes these nodes in response to spatial resolution requirements of the solution field and then updates the solution field based on the resulting distribution of nodes. DSAGA-PPM is implemented dynamically to resolve any evolving solution features. Tests with model problems demonstrate that DSAGA-PPM calculates advection much more accurately than the corresponding static grid algorithm (SGA-PPM) and, therefore, would assure more accurate starting concentrations for chemistry calculations. For example, after one revolution of four rotating cones, 87% of each of the cone peaks is retained using DSAGA-PPM while only 63% is retained using SGA-PPM. The root-mean-square errors in DSAGA-PPM results are about 4-5 times lower than those in the corresponding SGA-PPM results. Tests with reacting species and sources demonstrate that DSAGA-PPM provides the needed solution resolution. In simulations of a rotating and reacting conical puff, the root-mean-square errors in DSAGA-PPM results are about 4-6 times lower than those in the corresponding SGA-PPM results. In simulations of a power plant plume, the DSAGA-PPM solution reflects the early, the intermediate, and the mature stages of plume development; these stages are not seen in the corresponding SGA-PPM solution. Finally, it is demonstrated that DSAGA-PPM provides an accurate description of the ozone production resulting due to dynamic interactions between emissions from two power plants and an urban area. In general, these results reflect that DSAGA-PPM is able to provide accurate spatial and temporal resolution of rapidly changing and complex concentration fields. Performance achieved by DSAGA-PPM in model problem simulations indicates that it can provide accurate air quality modeling solutions at costs 10 times less than those incurred in obtaining equivalent static grid solutions.







Aerospace Engineering